Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.
Elena L CáceresNicholas C MewMichael J KeiserPublished in: Journal of chemical information and modeling (2020)
Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug-target relationships.
Keyphrases
- machine learning
- neural network
- big data
- electronic health record
- adverse drug
- deep learning
- healthcare
- minimally invasive
- artificial intelligence
- virtual reality
- randomized controlled trial
- climate change
- clinical trial
- data analysis
- double blind
- transcription factor
- cerebral ischemia
- placebo controlled
- binding protein